Systems, methods, apparatus, and articles of manufacture to identify times at which live media events are distributed

ABSTRACT

Systems, methods, apparatus, and articles of manufacture to identify times at which live media events are distributed are disclosed. An example method includes collecting feed data representative of source feeds associated with respective live media events, collecting distribution data representative of a plurality of distributions of the source feeds, and comparing, using a processor, the distribution data to the feed data to generate a list identifying times at which the live media events were distributed via corresponding distributions.

RELATED APPLICATIONS

This patent claims priority to U.S. Provisional Patent Application No.61/593,088, filed Jan. 31, 2012, the entirety of which is herebyincorporated by reference.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement, and, moreparticularly, to systems, methods, apparatus, and articles ofmanufacture to identify times at which live media events aredistributed.

BACKGROUND

In addition to scheduled, pre-recorded programming, television networkspresent live broadcast events, such as sports events or live newsevents. In some cases, the live broadcast events are scheduled to startat a first time and end at a second time, but in fact begin and/or endat different times than their scheduled times. This can occur when, forexample, the start time of a later event must be later than the end timeof an earlier event (e.g., consecutive, co-located sports matches). Thiscan also occur when a first event runs long (e.g., goes into overtime,extra innings, etc.).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example system to identify times atwhich live media events are distributed, constructed according to theteachings of this disclosure.

FIG. 2 is a block diagram of an example apparatus that may be used toimplement the distribution data resolver of FIG. 1.

FIGS. 3A-3C illustrate an example process that may be performed by thesystem of FIG. 1 to identify times at which live media events aredistributed.

FIG. 4 is a flowchart representative of example machine readableinstructions which may be executed by a logic circuit to identify timesat which live media events are distributed.

FIG. 5 is a flowchart representative of example machine readableinstructions which may be executed by a logic circuit to collect feeddata for live media events.

FIG. 6 is a flowchart representative of example machine readableinstructions which may be executed by a logic circuit to collectdistribution data.

FIG. 7 is a flowchart representative of example machine readableinstructions which may be executed by a logic circuit to generate a listof times at which live media events were distributed.

FIG. 8 illustrates an example process to identify times at whichmultiple live network events are distributed via respectivedistributions.

FIG. 9 is a block diagram of an example processor platform capable ofexecuting the instructions of FIGS. 4, 5, 6, and/or 7 to implement thesystems and apparatus of FIGS. 1, 2, 3A-3C, and/or 8.

DETAILED DESCRIPTION

Advertisers are interested in the viewership or audience composition oflive sporting events, because many such sporting events have asubstantial audience. Additionally, advertisers are interested in theaudience of television programming prior to and/or following the livemedia events. Audience measurement services, such as those provided byThe Nielsen Company, LLC, measure the audiences of programs and providethe audience information (e.g., size and demographics of an audience ofa program) to the advertisers and/or advertisement or advertisement timeslot sellers to enable these parties to make informed advertisingdecisions.

In some cases, audience measurement is performed based, at least inpart, on scheduled start and end times. However, the end time of a priorprogram and/or the start time of the subsequent program may be differentthan the scheduled time. Differences between the scheduled live mediaevent start and/or end times are then manually entered with cooperationbetween the audience measurement service provider and the broadcasters.Such manual methods can be costly and inconvenient.

To overcome these deficiencies, example systems, methods, apparatus, andarticles of manufacture disclosed herein automatically identify times atwhich live media events are broadcast. In a disclosed example method,one or more feed data collectors collect feed data representative ofsource feeds associated with a plurality of live media events.Distribution data collectors also collect distribution datarepresentative of respective distributions. In some examples,distributions occur in multiple geographical areas, such as localbroadcast areas. In some other examples, distributions are independentof geographical location, such as with Internet or satellite delivery. Adistribution data resolver compares the distribution data to the feeddata to generate a list, where the list identifies times at which thelive media events were distributed via corresponding distributions. Suchexample methods lower costs and may further increase the accuracy and/orprecision with which audience information is collected for live mediaevents.

Most local broadcast networks (e.g., network-affiliated televisionstations) have control over which of a set of live event feeds (e.g.,source feeds) are transmitted by the local broadcast network. In somecases, the manager of a distributor (e.g., a local broadcast network)that is scheduled to show a first live media event may decide to changefrom distributing (e.g., broadcasting, streaming, transmitting, etc.) afirst network feed to distributing a second, different network feed.This type of switch is referred to herein as an “on-the-fly” switch,because the switch from one event feed to a second event feed isperformed during the event feed. An on-the-fly switch may occur to, forexample, attract or retain audience members when the first, scheduledlive media event is less interesting than the second live media event.The audience measurement service records the change in programming toappropriately credit the live media event with the audience exposure.

In some examples disclosed herein, a live media event identifiercompares a code embedded or otherwise present in and/or with adistribution to a code embedded or otherwise present in and/or with thefeed data to identify the distributed source feed. In some otherexamples, the live media event identifier compares a code embedded orotherwise present in and/or with the distribution to a library of codesto identify the source feed.

While examples described below refer to broadcast regions, broadcasters,and broadcasts, the examples may be applied to any type of contentdistribution. For example, any one or more of over-the-air broadcasting,cable delivery, satellite transmission, Internet delivery (e.g., digitalstreaming), and/or any other distribution media may be used. A localbroadcast or local distribution refers to a distribution to a particulargeographical area, and does not necessarily refer to any sizes ofgeographical regions. A live media event refers to any type of live(e.g., real-time or near real-time) content distribution event, and isnot restricted to any particular type of distribution medium.

FIG. 1 is a block diagram of an example system 100 constructed inaccordance with the teachings of this disclosure to identify times atwhich live media events are distributed. The example system 100 of FIG.1 is implemented across multiple broadcast regions 102, 104. Each of themultiple broadcast regions 102, 104 includes one or more local orregional broadcast stations 106, 108 associated with the same (e.g.,national, regional) network. The example regional broadcast stations106, 108 have contractual rights to broadcast the same feed data and/orsets of feed data (e.g., sporting events, sets of sporting events, newsevents, etc.) to the respective broadcast regions 102, 104. The exampleregional broadcast stations 106, 108 may distribute programming (e.g.,video and/or audio content such as television and/or radio broadcasts)and live media events to regional audiences via one or more ofover-the-air broadcasting, cable delivery, satellite broadcasting,Internet delivery, and/or any other distribution media that may be usedto deliver a event feed to audience members.

Each example broadcast region 102, 104 of FIG. 1 includes one or moremedia monitoring sites 110, 112. In some examples, the media monitoringsites 110, 112 may be substantially any location within a broadcastregion 102, 104 capable of receiving content (e.g., television and/orradio signals, digital streaming, etc.), such as panelist householdsand/or non-panelist locations such as retail locations, commerciallocations, cable television distributors, network affiliate broadcastsites, and/or any other non-panelist location. A panelist household isassociated with audience measurement panelists statistically selected byan audience measurement service provider (such as The Nielsen Company,LLC). The example media monitoring sites 110, 112 include televisions114, 116 (and/or tuning devices such as cable and/or satellite set topboxes, digital media devices, video game consoles, etc.) and/or othermedia presentation devices (e.g., computers with television tuners,cable and/or satellite set top boxes, digital media devices, radios,stereos, etc.) via which broadcasts from the regional broadcast stations106, 108 may be tuned and/or presented.

The example regional broadcast stations 106, 108 are provided with liveevent feeds 118, from which the regional broadcast stations 106, 108 mayselect for distribution to the broadcast regions 102, 104 (e.g., to themedia monitoring sites 110, 112). To identify which live media events inthe live event feed(s) 118 are transmitted to the media monitoring sites110, 112 in the different broadcast regions 102, 104, the example system100 includes distribution data collectors 120, 122, a feed datacollector 124, and a distribution data resolver 126.

The example distribution data collectors 120, 122 of FIG. 1 collectdistribution data received and displayed on the example presentationdevices (e.g., televisions) 114, 116. For example, the distribution datacollectors 120, 122 collect data representative of the local broadcasts,such as one or more signatures of the audio and/or the video in thelocal broadcasts and/or codes or watermarks that are embedded in theaudio and/or in the video. A signature is a descriptor that accuratelycharacterizes signals for the purpose of matching, indexing, or databaseretrieval. A code or watermark is information embedded in or includedwith signals that, when decoded, conveys the information. Codes orwatermarks are often embedded in a signal so as to be imperceptible to ahuman (e.g., an inaudible code or watermark embedded in an audiosignal). Codes or watermarks may include metadata (e.g., dataidentifying the content and/or information associated with the content)that is contained within a data stream of the content. An example ofmetadata includes content identification data contained within a MotionPicture Experts Group (MPEG) digital data stream in private and/or userdata fields.

The example distribution data collectors 120, 122 include respectivesignature generators 128, 130 and respective code extractors 132, 134.The example signature generators 128, 130 generate multiple signaturesrepresentative of the content presented by the presentation devices 114,116 (e.g., content displayed on the televisions). These signatures maybe compared to signatures generated from known content to match thedisplayed content to the known content. The example code extractors 132,134 extract codes present in the audio content and/or from the videocontent presented by the presentation devices 114, 116 (e.g., contentdisplayed by the televisions), where the codes have been previouslyinserted into and/or transmitted with the content to identify one ormore of a station name, a broadcaster name, a content owner, or aprogram name.

The distribution data collectors 120, 122 transmit any generatedsignatures and/or extracted codes to the example distribution dataresolver 126. For example, transmission may occur through a return pathfrom the distribution data collectors 120, 122 via a data network, acellular network, a telephony network, and/or any other return path.

The example feed data collector 124 of FIG. 1 includes a signaturegenerator 136. The feed data collector 124 monitors the live event feeds118 and generates one or more signatures 138 a, 138 b (e.g., via thesignature generator 136) corresponding to each different live event feed118 a, 118 b. Any appropriate signaturing technique may be used togenerate the example signatures 138 a, 138 b from the live event feeds118 a, 118 b. The example feed data collector 124 is also provided withinformation identifying one or more of a station name, a distributorname, a content owner and/or a program name for each of the examplefeeds 118 a, 118 b. The feed data collector 124 provides the signatures138 a, 138 b and the identifying information for the corresponding feeds118 a, 118 b to the example distribution data resolver 126.

The example distribution data resolver 126 compares the distributiondata (e.g., received from the distribution data collectors 120, 122) tothe feed data (e.g., received from the feed data collector 124) togenerate a list identifying times at which the live media eventscorresponding to the feeds 118 a, 118 b which were distributed viacorresponding distributors. Using this information, an audiencemeasurement system can accurately determine an audience for each of thelive media events when, for example, one or more distributors (e.g.,regional broadcast stations 106, 108) switches on-the-fly between liveevent feeds 118 a, 118 b. In some examples, the system 100 of FIG. 1determines the audience for the live media events when one or moreregional distributors 106, 108 temporarily and/or repeatedly switchbetween the live event feeds 118 a, 118 b (e.g., during switches betweengames in the NCAA men's' basketball tournament, between NationalFootball League games, etc.). The example distribution data resolver 126can automatically determine the distribution times for the distributionswithout requesting additional information from a distributor (e.g.,without requesting information via manual processes on whether anon-the-fly switch or other change in distribution has occurred).

While the example of FIG. 1 illustrates the regional distributors 106,108, the example regional distributors 106, 108 may be any type ofcontent distribution medium. For example, the live event feed(s) 118 mayadditionally or alternatively be distributed via over-the-airbroadcasting, cable delivery, satellite transmission, Internet delivery(e.g., digital streaming), and/or any other past, present, and/or futuredistribution media.

While only one feed data collector 124 is shown in the example of FIG.1, multiple feed data collectors 124 may be employed. For example, feeddata collectors 124 may be located in each “on site” truck uploadingcontent from specific sites of sporting or other live events, at onlinestreaming web servers, and/or any other locations from which feed datamay be transmitted.

FIG. 2 is a block diagram of an example apparatus 200 that may be usedto implement a distribution data resolver (e.g., the distribution dataresolver 126 of FIG. 1) and/or may be used to compare distribution data(e.g., received from the distribution data collectors 120, 122 ofFIG. 1) to the feed data (e.g., received from the feed data collector124 of FIG. 1) to generate a list identifying times at which the livemedia events corresponding to the feeds 118 a, 118 b were distributedvia corresponding distributors. The example apparatus 200 of FIG. 2includes a signature comparator 202, a code comparator 204, a signaturelibrary 206, a code library 208, a list generator 210, and acommunication device 212.

The example signature comparator 202 of FIG. 2 receives (e.g., via thecommunication device 212) distribution data including signatures ofdistributed content and feed data including signatures of live eventfeeds. In the example of FIG. 2, the distribution data and feed datareceived by the signature comparator 202 include signatures generatedusing the same or similar methods so that the signatures can be comparedto determine whether the same content or portion(s) of the same contentare represented by both compared signatures. For example, the signaturecomparator 202 may compare signatures generated using methods thatgenerate signatures of portions of the content every few seconds,multiple times per second, multiple times per minute, and/or any othersuitable time period. In some examples, the signature comparator 202compares first sets of signatures of the content that are generatedusing a first method and compares second sets of signatures of thecontent that are generated using a second method. The signaturecomparator 202 may use multiple signature comparisons to, for example,increase confidence that the compared contents match.

If the signature comparator 202 determines that the signatures match,the signature comparator 202 stores information regarding the match inthe list generator 210. Example information that may be stored in thelist generator 210 includes a timestamp of the content presentation(obtained from, for example, the distribution data and/or the feed dataor from a clock coupled to the computer), identifying information aboutthe content (e.g., a name of the live media event), a network name, anetwork identifier, a distribution region, a distributor name, a stationname, an owner of the live media event content, and/or any otheridentifying and/or logging information.

In some examples, signatures for the distribution data and/or signaturesfor the feed data are stored in the signature library 206 as they arereceived. The signature library 206 maps signatures to informationassociated with the content, such as a timestamp of the content fromwhich the signature was generated (obtained from, for example, thedistribution data and/or the feed data), identifying information aboutthe content (e.g., a name of the live media event), a distributiontime/date, a network name, a network identifier, a distribution region,a distributor name, a station name, an owner of the live media eventcontent, and/or any other identifying and/or logging information. Theidentifying information may be used to index the example signaturesand/or may be retrieved and/or provided to the list generator 210, suchas when a feed data signature matching a distribution data signature isfound in the signature library and/or vice versa. In some such examples,the signature comparator 202 compares signatures received viadistribution data to signatures previously received in feed data thathas been stored in the signature library 206 to attempt to find amatching signature.

The example code comparator 204 of FIG. 2 receives the distribution data(e.g., from the distribution data collectors 120, 122 of FIG. 1) andattempts to identify one or more codes present in the distribution data.For example, the distribution data collectors 120, 122 of FIG. 1 extractcodes or watermarks (if any) embedded in the example content displayedby the respective information presentation devices (e.g., televisions114, 116). Some such codes and/or watermarks may provide identifyinginformation regarding the content, such as a distributor name, a networkname, a content identification (e.g., a program name), a timestamp(e.g., a date and/or time of day) and/or other information. The exampledistribution data collectors 120, 122 transmit the codes and/orwatermarks to the distribution data resolver 126 of FIG. 1 (e.g., at thecode comparator 204). The code comparator 204 determines whether thecodes and/or watermarks match known codes and/or watermarks (e.g., inthe code library 208).

The example code library 208 of FIG. 2 maps codes and/or watermarks topre-determined information associated with live media events, such asone or more of a station name, a broadcaster or distributor name, acontent owner, a program name, and/or any other information associatedwith a live media event. For example, the code library 208 may storenewly-generated codes and the information corresponding to the codes.Thus, when the example code comparator 204 receives a code or awatermark in distributed information, the code comparator 204 determineswhether the code or watermark matches any of the codes or watermarksstored in the example code library 208. In some examples, the codelibrary 208 receives codes embedded into feeds for live media eventsfrom the feed data collector 124 of FIG. 1. In some examples, the codesare an agreed station identifier and a time/date stamp, therebyautomatically mapping content to a distributor and time of distribution.

If a code present in the distribution data matches a code in the library208, the example code comparator 204 provides the code, informationstored in the code library 208 that corresponds to the code, and/orother identifying information for the distribution, such as a timestampof the content presentation (obtained from, for example, thedistribution data, the code, and/or the feed data), identifyinginformation about the content (e.g., a name of the live media event), anetwork name, a network identifier, a distribution region, a distributorname, a station name, an owner of the live media event content, adistributor, and/or any other identifying and/or logging information.

The example list generator 210 of FIG. 2 receives informationcorresponding to matching signatures and/or corresponding to identifiedcodes and generates a list that identifies times at which the live mediaevents were distributed via a corresponding distribution. In someexamples, the list includes information representative of events, suchas when a regional distributor 106, 108 starts distributing a first liveevent feed, ends distribution of a first live event feed, changes fromdistributing the first live event feed to distributing a second liveevent feed (e.g., scheduled and/or on-the-fly), and/or any otherinformation relevant to identifying times and/or regions at which liveevents are distributed. In some examples, either the first live eventfeed or the second live event feed may be replaced by a non-live feed(e.g., a recorded program) and/or by a delayed feed (e.g., a nearly-livefeed that is recorded and time shifted). The information in the listgenerated by the example list generator 210 advantageously providesdetailed information about the live media events provided to an audiencewithout manually revising a distribution schedule. Thus, if adistributor (e.g., television broadcaster) in the broadcast region 104of FIG. 1 presents the Northwestern University basketball team playingthe University of Illinois from 7:30 P.M. to 8:02 P.M., switches todistributing a game between the University of Wisconsin and MarquetteUniversity at 8:02 P.M. until 8:04 P.M. and then, at 8:04 P.M., returnsto the Northwestern game, the example system of FIGS. 1 and/or 2 willdetect and record the presentation of the two different games on thesame distribution channel without manual intervention. As a result,audience allocation to specific events (e.g., the Northwestern gameversus the Wisconsin game) can be performed efficiently and accurately.

The example communication device 212 communicates with external devices(e.g., the feed data collector 124, the distribution data collectors120, 122, etc.). For example, the communications device 212 may includeany type(s) of wired or wireless electronic communications interface,such as WiMax, Wifi, Ethernet, cellular radio, and/or any other past,present, or future communications technology.

FIGS. 3A-3C illustrate an example system 300 to perform a process toidentify times at which live media events are distributed (e.g., via oneor more distributors, in one or more broadcast regions such as thebroadcast regions 102, 104 of FIG. 1, etc.).

FIG. 3A illustrates an example system 300 in which several broadcaststations A, B, C, and D are each distributing one of multiple differentnetwork feeds 302, 304 (e.g., live media events). In the example of FIG.3A, broadcast stations A, B are distributing the first network feed 302and broadcast stations C, D are distributing the second network feed304. The distribution data resolver 126 of FIG. 1 receives the networkfeeds 306, 308 (e.g., feed data representative of the network feeds 306,308). The distribution data resolver 126 also receives station data 310representative of scheduled broadcasts by the stations A, B, C, D (e.g.,distribution data from distribution data collectors).

Based on the station data 310, the example distribution data resolver126 compares the network feeds 302, 304 to the station data 310 todetermine which of the network feeds 302, 304 are being broadcast byeach of the example stations A, B, C, D. In the example of FIG. 3A, thenetwork feeds 302, 304 are represented by one or more signatures 306,308 of the content of the network feeds 302, 304.

Based on the determination of which of the stations A, B, C, D aredistributing each network feed 302, 304, the example distribution dataresolver 126 sorts the stations A, B, C, D into different station matchgroups 312, 314. The stations A, B in the first match group 312 aredetermined to be distributing the network feed 302 based on the stationdata 310 and/or the signatures 306. Similarly, the stations C, D in thesecond match group 312 are determined to be distributing the networkfeed 304 based on the station data 310 and/or the signatures 308.

The example distribution data resolver 126 provides the station matchgroups 312, 314 and/or other live media event information to a monitor316. The monitor 316 may be viewed by a person monitoring the live mediaevents and the stations showing each of the live media events. Themonitor 316 displays content (e.g., video) 318, 320 of the network feeds302, 304 (e.g., content from the network feeds 302, 304) in respectivelocations on the monitor 316. The example monitor 316 further displayswhich of the example stations A, B, C, and D are displaying each of thenetwork feeds 302, 304. In the example of FIGS. 3A-3C, the monitor 316arranges the displays of the stations to group the station content bythe feed being broadcast. Thus, the example monitor 316 displays content(e.g., video) 322, 324, 326, 328 of the respective feeds 302, 304 beingbroadcast by each of the example stations A, B, C, D in association withlabels and/or locations allocated to particular ones of the stations A,B, C, or D. In the example of FIG. 3A, the content 322, 324 for stationsA, B is positioned near the content 318 and the content 326, 328 forstations C, D is positioned near the content 320.

FIG. 3B illustrates the example system 300 at a second time later thanthe time illustrated in FIG. 3A. In FIG. 3B, the distribution dataresolver 126 has determined that the live media event broadcast bystation C no longer matches the station match group 314 and/or the livemedia event broadcast by station D in the station match group 314. Forexample, the signature comparator 202 of FIG. 2 may fail to matchsignatures of the distribution data associated with station C withsignatures of distribution data associated with station D and/or failsto match the signatures 308 for the network feed 304. In this example,the distribution data associated with the stations C, D are generated byrespective distribution data collectors that are in the same broadcastregions as the respective stations C, D.

In response to determining that station C no longer matches the stationmatch group 314, the example distribution data resolver 126 (e.g., viathe signature comparator 202) determines whether the station C matchesany other station match group (e.g., the station match group 312). Inthe example of FIG. 3A, the station C has switched on-the-fly to thenetwork feed 302. As a result, the example distribution data resolver126 determines that the station C matches one or both of the stations A,B in the station match group 312.

FIG. 3C illustrates the example system 300 at a third time after thesecond time of FIG. 3B. In the example of FIG. 3C, the distribution dataresolver 126 provides the updated station match group information to theexample monitor 316. In response, the monitor 316 displays the station Cto display content 326 for the feed 302 and changes the position of thecontent 326 corresponding to Station C to be near the correspondingcontent 318.

Additionally, the example monitor 316 and/or the example distributiondata resolver 126 notifies a data updater 330 to update data for thestation C. For example, when the data updater 330 is notified as to theupdate, the data updater 330 may update a distribution or audiencemeasurement record to reflect the change from the feed 304 to the feed302 by station C. Accordingly, the example system 300 may beadvantageously used to automatically notify and/or update distributionand/or audience measurement records to reflect on-the-fly changes todistribution of live media events.

While example manners of implementing the distribution data collectors120, 122, the feed data collector 124, and the distribution dataresolver 126 of FIG. 1 have been illustrated in FIGS. 2 and 3A-3C, oneor more of the elements, processes and/or devices illustrated in FIGS.1, 2, and 3A-3C may be combined, divided, re-arranged, omitted,eliminated and/or implemented in any other way. Further, the feed datacollector 124, the distribution data collectors 120, 122, the signaturegenerators 128, 130, 136, the code extractors 132, 134, the signaturecomparator 202, the code comparator 204, the signature library 206, thecode library 208, the list generator 210, the communication device 212,the distribution data resolver 126, and/or the apparatus 200, the dataupdater 330 and/or, more generally, the example distribution dataresolver 126 and/or the example apparatus 200 of FIGS. 1, 2, and 3A-3Cmay be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the feed data collector 124, the distribution data collectors120, 122, the signature generators 128, 130, 136, the code extractors132, 134, the signature comparator 202, the code comparator 204, thesignature library 206, the code library 208, the list generator 210, thecommunication device 212, the distribution data resolver 126, and/or theapparatus 200, the data updater 330 and/or, more generally, the exampledistribution data resolver 126 and/or the example apparatus 200 could beimplemented by one or more circuit(s), programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)),etc. When any of the apparatus or system claims of this patent are readto cover a purely software and/or firmware implementation, at least oneof the feed data collector 124, the distribution data collectors 120,122, the signature generators 128, 130, 125, the code extractors 132,134, the signature comparator 202, the code comparator 204, thesignature library 206, the code library 208, the list generator 210, thecommunication device 212, the distribution data resolver 126, and/or theapparatus 200, and/or the data updater 330 hereby expressly defined toinclude a tangible computer readable medium such as a memory, DVD, CD,Blu-ray, etc. storing the software and/or firmware. Further still, theexample distribution data resolver 126 and/or the apparatus 200 of FIGS.1 and 2 may include one or more elements, processes and/or devices inaddition to, or instead of, those illustrated in FIGS. 1 and 2, and/ormay include more than one of any or all of the illustrated elements,processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the distribution data collectors 120, 122, the feed datacollector 124, and the distribution data resolver 126 of FIG. 1 areshown in FIGS. 4-7. In this example, the machine readable instructionscomprise one or more programs for execution by a processor such as theprocessor 912 shown in the example processor platform 900 discussedbelow in connection with FIG. 9. The programs may be embodied insoftware stored on tangible computer readable media such as a CD-ROM, afloppy disk, a hard drive, a digital versatile disk (DVD), a Blu-raydisk, or a memory associated with the processor 912, but the entireprogram and/or parts thereof could alternatively be executed by a deviceother than the processor 912 and/or embodied in firmware or dedicatedhardware. Further, although the example programs are described withreference to the flowcharts illustrated in FIGS. 4-7, many other methodsof implementing the example distribution data collectors 120, 122, theexample feed data collector 124, and/or the example distribution dataresolver 126 may alternatively be used. For example, the order ofexecution of the blocks may be changed, and/or some of the blocksdescribed may be changed, eliminated, or combined.

As mentioned above, any of the example processes of FIGS. 4-7 may beimplemented using coded instructions (e.g., computer readableinstructions) stored on a tangible computer readable medium such as ahard disk drive, a flash memory, a read-only memory (ROM), a compactdisk (CD), a digital versatile disk (DVD), a cache, a random-accessmemory (RAM) and/or any other storage media in which information isstored for any duration (e.g., for extended time periods, permanently,brief instances, for temporarily buffering, and/or for caching of theinformation). As used herein, the term tangible computer readable mediumis expressly defined to include any type of computer readable storageand to exclude propagating signals. Additionally or alternatively, theexample processes of FIGS. 4-7 may be implemented using codedinstructions (e.g., computer readable instructions) stored on anon-transitory computer readable medium such as a hard disk drive, aflash memory, a read-only memory, a compact disk, a digital versatiledisk, a cache, a random-access memory and/or any other storage media inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, brief instances, for temporarily buffering, and/orfor caching of the information). As used herein, the term non-transitorycomputer readable medium is expressly defined to include any type ofcomputer readable medium and to exclude propagating signals. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended. Thus, a claim using “at least” as thetransition term in its preamble may include elements in addition tothose expressly recited in the claim.

The program of FIG. 4 begins at block 402, in which a feed datacollector (e.g., the feed data collector 124 of FIG. 1) collects feeddata from source feeds for live media event(s). For example, the feeddata may include collecting signatures and/or embedded codes from anaudio and/or video content feed. An example method to implement block402 and/or the feed data collector 124 is described below andillustrated in FIG. 5.

The example method 400 of FIG. 4 collects distribution data ofdistributions of source feeds (e.g., via the distribution datacollectors 120, 122 of FIG. 1) (block 404). For example, thedistribution data collectors 120, 122 of FIG. 1 generate signatures andextract embedded codes from content that is presented via respectivepresentation devices. The distribution data collectors 120, 122 may beassociated with media monitoring sites 110, 112 (e.g., panelisthouseholds, locations not associated with panelist households). The datacollectors 120, 122 are located in different broadcast regions 102, 104(e.g., different geographical areas). Thus, the example distributiondata collectors 120, 122 may tune into the same network of channels butmay receive different content based on which of a plurality of liveevent feeds is being distributed via a respective distributor (e.g., ina respective broadcast region 102, 104). An example method to implementblock 404 and/or the distribution data collectors 120, 122 is describedbelow and illustrated in FIG. 6.

The example distribution data resolver 126 compares the collecteddistribution data to the collected feed data to generate a list of timesat which the live media events were distributed via correspondingdistributions (block 406). For example, the distribution data resolver126 of FIG. 1 receives the collected feed data from the feed datacollector 124 and the collected distribution data from the distributiondata collectors 120, 122, and determines which of multiple feeds 138 a,138 b were transmitted via each of respective distributors. An examplemethod to implement block 406 and/or the distribution data resolver 126is described below and illustrated in FIG. 6.

The example method 400 may then end and/or iterate to continueidentifying what times event feeds are distributed.

FIG. 5 is a flowchart representative of example machine readableinstructions 500 which may be executed by a logic circuit such as theprocessor 912 of FIG. 9 to collect feed data for live media events. Theexample instructions 500 may be executed to implement the example feeddata collector 124 and/or the example signature generator 136 of FIG. 1.

The method 500 of FIG. 5 begins by receiving (e.g., at the feed datacollector 124 of FIG. 1) network feeds for multiple live media events(block 502). For example, the feed data collector 124 of FIG. 1 receivesfeeds 118 a, 118 b from a source of live event feeds 118, such asrecordings of multiple, concurrent sporting events.

The example feed data collector 124 generates signatures 138 a, 138 brepresentative of each of the network feeds 118 a, 118 b (e.g., via thesignature generator 136) (block 504). The signatures generated by theexample signature generator 136 are representative of blocks of audio,video, and/or both audio and video in the network feed 118 a, 118 b forwhich the signatures are generated. In some examples, the generatedsignatures represent overlapping blocks of audio and/or video.

The example feed data collector 124 further extracts codes that arepresent in the source feeds (e.g., live event feeds 118 a, 118 b) (block506). For example, inaudible or substantially inaudible codes (e.g.,audio-masked codes or watermarks) may be embedded into the live eventfeeds 118 a, 118 b to provide information to an audience measurementdevice or system measure an audience of television programs, movies,games, and/or any other types of media being watched by an audience.These codes can provide, for example, information identifying theprogram or content being presented, the channel on which it ispresented, timestamp information, ownership information, and/or othertypes of information. The example feed data collector 124 extracts thecodes and/or watermarks to determine the codes present in the eventfeeds. To perform the detection, the example feed data collector 124 isprovided with one or more decoders to perform one or more procedures todetect codes in the source feeds (e.g., the live event feeds 118 a, 118b).

In some other examples, the feed data collector 124 and/or thedistribution data resolver 126 of FIG. 1 are provided with a prioriknowledge of the codes and/or watermarks to be present in the networkfeeds 118 a, 118 b. In these examples, the feed data collector 124 doesnot attempt to extract codes or watermarks from the feeds 118 a, 118 band block 506 may be omitted.

The example feed data collector 124 of FIG. 1 transmits generated feedsignatures and any extracted code(s) to a distribution data resolver,such as the distribution data resolver 126 of FIG. 1 (block 508). Forexample, the feed data collector 124 may communicate with the exampledistribution data resolver 126 via a network such as the Internet or alocal area network (LAN).

The example feed data collector 124 determines if the ends of the livemedia events has been reached (block 510). If one or more of the livemedia events are continuing (block 510), the example feed data collector124 returns to block 502 to continue collecting feed data.

In some examples, one or more of the blocks 502-510 are runsimultaneously or substantially simultaneously on sequential blocks ofdata. For example, a first block of audio and video received on a firstnetwork feed 118 a is received (block 502). Signatures of the firstblock are then generated at block 504. While block 504 is beingperformed on the first block, a second block of audio and video may bereceived at block 502. Thus, blocks 502-506 may operate on 2 or moreblocks of audio and video substantially simultaneously, while block 508is performed on signatures and code(s) for yet another block of data. Inother words, multiple instances of the blocks executing, for instance,in parallel processing threads may exist.

FIG. 6 is a flowchart representative of example machine readableinstructions 600 which may be executed by a logic circuit to collectdistribution data. The example instructions 600 may be executed toimplement the example distribution data collectors 120, 122, the examplesignature generators 128, 130, and/or the example code extractors 132,134 or FIG. 1.

The instructions 600 of FIG. 6 being by monitoring distributed audioand/or video (block 602). For example, monitoring distributed audio mayinclude receiving analog and/or digital audio and/or video signals thatare output from a television or other media presentation device (e.g.,the televisions 114, 116 of FIG. 1) tuned to (e.g., in the same locationas, receiving streaming data from, etc.) the distribution data collector120, 122. In some examples, such analog and/or digital audio and/orvideo signals may be collected (e.g., from a set top box or other signalreceiver) even when the television is not actually displaying the videoand/or playing the audio (e.g., when the television is off or in a mutedstate). In some other examples, the distribution data collector 120, 122includes a microphone to collect audio actually output from a televisionor other presentation device so muting would interfere with the datacollection.

The example instructions 600 generate signatures (e.g., via thesignature generator 128, 130) representative of the distributed audioand/or video (e.g., video played via a media presentation device) (block604). For example, the signature generators 128, 130 may be configuredto generate the same and/or compatible types of signatures (e.g.,signatures generated using the same methods) as the signature generator136. Thus, the signatures generated from the network feeds 118 a, 118 band signatures generated from the distributed audio and/or video willresult in matching signatures for corresponding (e.g., same or similar)blocks of audio and/or video.

The example instructions 600 also extract (e.g., via the code extractors132, 134) code(s) (if present) from the audio and/or video (block 606).For example, the code extractors 132, 134 may determine whether codes orwatermarks are embedded in the distributed audio and/or video. If codesor watermarks are recognized, the example code extractors 132, 134extract and log the codes to be sent to the distribution data resolver126.

The example distribution data collector 120, 122 transmits thesignatures and extracted codes to a distribution data resolver (e.g.,the distribution data resolver 126 of FIG. 1) (block 608). For example,the distribution data collector 120, 122 may transmit signaturesgenerated by the signature generator 128, 130 and codes extracted by thecode extractor 132, 134 periodically, aperiodically, on request by thedistribution data resolver 126, and/or whenever a signature is generatedand/or a code extracted, for the duration of a live media event. Forexample, the distribution data collector 120, 122 may store signaturesand codes and transmit stored signatures and codes every few minutesduring the live media event. If the distribution data collector 120, 122does not transmit the information often enough, the distribution dataresolver 126 may not determine which network feeds 118 a, 118 b arebeing distributed via each distributor with sufficient precision oraccuracy.

The example distribution data collector 120, 122 determines whether theend of the live media event has been reached (block 610). For example,the distribution data collector 120, 122 may identify an end codeembedded in the distributed audio and/or video, receive a notificationfrom the distribution data resolver 126, and/or otherwise determine thatthe live media event has ended. If the event has not ended (block 610),control returns to block 602 to continue monitoring the distributedaudio and/or video. If, on the other hand, the live media event hasended (block 610), the example instructions 600 of FIG. 6 may end and/oriterate for another live media event.

FIG. 7 is a flowchart representative of example machine readableinstructions 700 which may be executed by a logic circuit to generate alist of times at which live media events were distributed. The exampleinstructions 700 may be executed to implement the example distributiondata resolver 126 of FIG. 1 and/or the example apparatus 200 of FIG. 2.For ease of discussion, the example instructions of FIG. 7 will bediscussed below with reference to the example apparatus 200 to implementthe example distribution data resolver 126 in the example system 100 ofFIG. 1.

The example apparatus 200 receives a list of scheduling for live mediaevents (block 702). For example, the apparatus 200 may receive the listof scheduling via the communication device 212. The list of schedulingincludes anticipated distributors and/or distributions (e.g., broadcastareas 102, 104) and corresponding times that the distributors are toshow particular source feeds, and may include codes to be embedded innetwork feeds of the live media events to be stored in the code library208, provided to the code comparator 204, and/or provided to the listgenerator 210.

The example list generator 210 adds the scheduling in the received listof scheduling to the example list of distribution times 214 (block 704).In some examples, the list generator 210 generates a tentative orpreliminary list of distribution times prior to generating a final listof distribution times. The preliminary list may then be modified priorto generating and/or outputting a final list of distribution times.

The example apparatus 200 receives (e.g., via the communications device212, the signature comparator 202, and/or the code comparator 204)receives signature(s) and code(s) from multiple distributions (e.g.,different broadcast regions 102, 104 of FIG. 1, different streamingsources, etc.) (block 706). For example, the apparatus 200 may receivesignature(s) and/or code(s) from distribution data collectors 120, 122located in different broadcast areas 102, 104. The signature(s) aregenerated by the distribution data collectors 120, 122 and the code(s)are extracted by the distribution data collectors 120, 122 based onwhich of multiple live media events are being distributed via therespective distributor.

The example apparatus 200 receives feed signature(s) and code(s) forlive media event feeds (e.g., via the communications device 212, thesignature comparator 202, and/or the code comparator 204) (block 708).For example, the apparatus 200 may receive signature(s) and/or code(s)for multiple network feeds 118 a, 118 b from the example feed datacollector 124 of FIG. 1. The signatures are generated by the feed datacollector 124 and the codes are extracted by the feed data collector 124from the network feeds 118 a, 118 b.

The example instructions 700 enter a for-loop 710 to process data foreach of the distributions to be measured (e.g., broadcast areas 102,104). In each iteration of the example loop 710, the apparatus 200selects one of the distributions. When the data from each of thedistributions has been processed using the loop 710, the example loop710 may end.

The apparatus 200 determines a scheduled live media event for theselected distribution (block 712). For example, the signature comparator202 or the code comparator 204 may select one of the distributions, anddetermine (e.g., from the schedule information) which of multiple sourcefeeds or live media events is scheduled to be distributed via theselected distribution. The example apparatus 200 then determines whetherthe scheduled feed for the distribution area corresponds to thedistribution data for the selected distribution area (block 714).

For example, the signature comparator 202 may determine whether one ormore signature(s) received from the distribution data collector 120 inthe distribution matches one or more signature(s) for the source feed118 a scheduled to be distributed (e.g., broadcast) in the broadcastarea 102. Additionally or alternatively, the example code comparator 204may determine whether one or more codes received from the distributiondata collector 120 in the broadcast area 102 matches one or more code(s)for the network feed 118 a scheduled to be distributed in the broadcastarea 102. In some examples, signatures and/or codes for advertisementperiods are excluded and/or at least a threshold number of signaturesand/or codes must match before the apparatus 200 determines that thescheduled feed data corresponds to the distribution data.

If the scheduled feed data does not correspond to the distribution data(block 714), the example apparatus 200 identifies which live networkevent is distributed via the selected distribution from the signature(s)and/or the code(s) (block 716). For example, the signature comparator202 may compare the signatures (received from distribution datacollectors 120, 122 in the selected broadcast area 102, 104) tosignatures stored in the signature library 206 for the available networkfeed(s). The example list generator 210 updates the list as anon-the-fly switch between a scheduled live media event and the actual,detected live media event (block 718).

If the scheduled feed data corresponds to (e.g., represents the sameaudio and/or video information as) the distribution data for theselected distribution (block 714), the example instructions 700 skipblocks 716 and 718, and selected a next distribution (if any remain tobe processed).

FIG. 8 illustrates an example process 800 to identify times at whichmultiple live media events are distributed via respective distributions.The example process 800 illustrated in FIG. 8 may be used in addition oras an alternative to the example processes described with reference toFIGS. 4-7. The example process 800 is implemented using the exampledistribution data resolver 126 of FIG. 1.

In the example process 800 of FIG. 8, the distribution data resolver 126receives a plurality of broadcast stations A-F 802-812 corresponding tothe same network (e.g., a television network that may show any of thesame set of live media events). In some examples, the distribution dataresolver 126 receives signatures of the audio and video distributed bythe respective broadcast stations A-F 802-812.

The example distribution data resolver 126 further receives multiplenetwork feeds 814, 816 representative of different live media events.Any combination of the feeds 814, 816 may be distributed by anycombination of the stations A-F 802-812. In some examples, thedistribution data resolver 126 receives signatures of the audio andvideo distributed by the respective broadcast stations A-F 802-812.

The example distribution data resolver 126 also receives schedule data818. The example schedule data 818 of FIG. 8 represents that stations A,B, and C 802-806 are to distribute (e.g., broadcast, air) the first feed814 and stations D, E, and F 808-812 are to distribute the second feed816 during an event time period 820.

During the event time period 820, the example distribution data resolver126 matches the broadcast stations A-F 802-812 to the feeds 814, 816according to the live media event(s) being distributed on the respectivestations A-F 802-812. For example, the distribution data resolver 126may match signatures of the stations A-F 802-812 to signatures of thefeeds 814, 816 to determine matches. In some examples, the distributiondata resolver 126 attempts to match each station A-F 802-812 first basedon the schedule data 818. In the example of FIG. 8, stations A, B, and C802-806 match the first feed 814 during a first period 822 and thestations D, E, and F 808-812 match the second feed 816 during a secondperiod 824. The first and second periods 822, 824 are sub-periods of theexample event time period 820 and may be partially or completelyoverlapping.

During a third period 826 (e.g., a third sub-period of the event timeperiod 820), the distribution data resolver 126 determines that none ofthe stations A, B, or C 802-806 match the first feed 814. This mayoccur, for example, when a break in the feed occurs (e.g., scheduled orunscheduled) in which each of the stations A, B, and C 802-806 ispermitted to sell advertising time on their respective stations (incontrast to showing a national advertisement). In response, thedistribution data resolver 126 invokes an automatic contentidentification process 828. The example automatic content identificationprocess 828 may be performed by the distribution data resolver 126and/or by a different entity (e.g., a different server, a differentcomputer, etc.). The automatic content identification process 828 hasaccess to a media content warehouse 830, which stores known content thatcan be used to identify unknown content via, for example, signatureand/or code matching. In the example of FIG. 8, the media contentwarehouse 830 includes, among other things, signatures corresponding toknown television commercials. The automatic content identificationprocess 828 uses the signatures in the media content warehouse 830 toidentify the content distributed by the stations A, B, and C 802-806during the third time period 826.

At a fourth time period 832, the example distribution data resolver 126determines that station D 808 no longer matches the second feed 816,while the stations E and F 810, 812 still match the second feed 816. Inresponse, the example distribution data resolver 126 performs anautomatic search process 834, in which the distribution data resolver126 searches the feeds 814, 816 to determine which of the feeds 814, 816is being distributed via the station D 808. In the example of FIG. 8,the automatic search process 834 determines that station D has changedfeeds to the first feed 814. As a result, the distribution data resolver126 records the change in the first feed 814 for station D 808 in block836. Recording the change may include, for example, recording anon-the-fly change for the station D 808.

As a result of the change of station D 808 to the first feed 814, thedistribution data resolver 126 determines that stations A, B, C, and D802-808 match the first feed 814 during a fifth period 838.Additionally, the distribution data resolver 126 determines thatstations E and F 810, 812 match the first feed 814 during a sixth period840. The example periods 838, 840 run to the end of the example eventtime period 820.

The example distribution data resolver 126 outputs feed distributiondata 842, which includes information representative of the matchesbetween the stations A-F 802-812 and the feeds 814, 816. In the exampleof FIG. 8, the feed distribution data 842 includes the start times andthe end times of the event time period 820 and the time periods 822,824, 826, 832, 838, and 840. The example distribution data 842 furtherincludes information representative of the on-the-fly switch identifiedby the automatic search process 834.

The time periods 822, 824, 826, 832, 838, and 842 of FIG. 8 are notnecessarily drawn to scale. For example, the time periods 826 and/or 832may be substantially shorter in length than the time periods 822, 824,838, and/or 840. Further, the process 800 may include more or fewer timeperiods than the time periods 822, 824, 826, 832, 838, and 842, more orfewer stations than the stations A-F 802-812, and/or more or fewer feedsthan the feeds 814, 816.

FIG. 9 is a block diagram of an example processor platform 900 capableof executing the instructions 400-700 of FIGS. 4-7 to implement the feeddata collector 124, the distribution data collectors 120, 122, thesignature generators 128, 130, 136, the code extractors 132, 134, thesignature comparator 202, the code comparator 204, the signature library206, the code library 208, the list generator 210, the communicationdevice 212, the distribution data resolver 126, and/or the apparatus200, the data updater 330, the automatic content identification process828, and/or the automatic search process 834 of FIGS. 1, 2, 3A-3C, and8. The processor platform 900 can be, for example, a server, a personalcomputer, a mobile phone (e.g., a cell phone), a personal digitalassistant (PDA), an Internet appliance, a digital video recorder, apersonal video recorder, a set top box, or any other type of computingdevice.

The system 900 of the instant example includes a processor 912. Forexample, the processor 912 can be implemented by one or moremicroprocessors, controllers, and/or logic circuits from any desiredfamily or manufacturer.

The processor 912 includes a local memory 913 (e.g., a cache) and is incommunication with a main memory including a volatile memory 914 and anon-volatile memory 916 via a bus 918. The volatile memory 914 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 916 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 914, 916 is controlledby a memory controller.

The processor platform 900 also includes an interface circuit 920 (e.g.,the communications device 212 of FIG. 2). The interface circuit 920 maybe implemented by any type of interface standard, such as an Ethernetinterface, a universal serial bus (USB), and/or a PCI express interface.

One or more input devices 922 are connected to the interface circuit920. The input device(s) 922 permit a user to enter data and commandsinto the processor 912. The input device(s) can be implemented by, forexample, a keyboard, a mouse, a touchscreen, a track-pad, a trackball,isopoint and/or a voice recognition system.

One or more output devices 924 are also connected to the interfacecircuit 920. The output devices 924 can be implemented, for example, bydisplay devices (e.g., a liquid crystal display, a cathode ray tubedisplay (CRT), a printer and/or speakers). The interface circuit 920,thus, typically includes a graphics driver card.

The interface circuit 920 also includes a communication device such as amodem or network interface card to facilitate exchange of data withexternal computers via a network 926 (e.g., an Ethernet connection, adigital subscriber line (DSL), a telephone line, coaxial cable, acellular telephone system, etc.).

The processor platform 900 also includes one or more mass storagedevices 928 for storing software and data. Examples of such mass storagedevices 928 include floppy disk drives, hard drive disks, compact diskdrives and digital versatile disk (DVD) drives. The mass storage device928 may implement the signature library 206 and/or the code library 208of FIG. 2.

The coded instructions 932 of FIGS. 4-7 may be stored in the massstorage device 928, in the volatile memory 914, in the non-volatilememory 916, and/or on a removable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that disclosed systems,methods, apparatus and articles of manufacture may be used to accuratelyand precisely identify times at which live media events are distributed.Example systems, methods, apparatus and articles of manufacturedisclosed herein can automatically provide rapid results and updatesregarding multiple distributions (e.g., multiple broadcast areas,multiple network feeds). Thus, the example systems, methods, apparatusand articles of manufacture can more rapidly and more efficiently creditlive media events and advertisements, as well as leading and/orfollowing programs or events, than known methods, particularly in thepresence of on-the-fly transitions between two or more live events.Further, the example systems, methods, apparatus and articles ofmanufacture increase the efficiency with which such data is collected byreducing an amount of manual labor used to acquire the live media eventinformation.

Example systems, methods, apparatus and articles of manufacturedisclosed herein may be used to determine whether high-definition andstandard-definition distributors (e.g., broadcasters) are simulcasting alive media event. For example, while a high-definition distributor andan associated standard-definition distributor in the same broadcastregion usually broadcasts the same content (at different resolutions),systems, methods, apparatus and articles of manufacture may determinewhen a high-definition distributor is presenting a first live mediaevent and a standard-definition distributor associated with thehigh-definition distributor is presenting a second, different live mediaevent. Similarly, example systems, methods, apparatus and articles ofmanufacture may be used to determine whether local broadcast satelliteoffices (e.g., local affiliates) and a parent network are simulcasting alive media event. For example, systems, methods, apparatus and articlesof manufacture may be used to detect when an affiliate is not presentingthe same content that the parent network is providing to affiliates.

Systems, methods, apparatus and articles of manufacture may also be usedto determine whether network feeds from geographically separatelocations (e.g., an Eastern United States feed, a Western United Statesfeed) are being properly monitored. For example, systems, methods,apparatus and articles of manufacture may be used to identify whethermanual monitoring data for feeds in different geographical locations isconsistent with actual presentation of feeds.

Although certain example methods, apparatus and articles of manufacturehave been described herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A method comprising: collecting feed datarepresentative of a plurality of source feeds being provided to adistributor, the source feeds being associated with respective livemedia events; collecting distribution data, the distribution data beingrepresentative of a plurality of distributions of the source feeds bythe distributor; and comparing, using a processor, the distribution datato the feed data to generate a list identifying an on-the-fly switchoccurring on a single channel between a first one of the live mediaevents and a second one of the live media events made by thedistributor, the first live media event being a live media eventscheduled to be transmitted by the distributor at a first time, andgenerating the list comprises logging that the second live media eventis actually transmitted by the distributor at the first time.
 2. Amethod as defined in claim 1, wherein the first live media event is afirst sporting event and the second live media event is a secondsporting event different from the first sporting event.
 3. A method asdefined in claim 1, wherein generating the list comprises comparing acode present in the feed data to a code present in the distributiondata.
 4. A method as defined in claim 3, wherein generating the listcomprises comparing a first signature associated with the feed data to asecond signature associated with the distribution data.
 5. A method asdefined in claim 3, wherein the code present in the distribution data iscollected at a reference station not present in a panelist household. 6.A method as defined in claim 1, wherein generating the list comprisescomparing a first signature associated with the feed data to a secondsignature associated with the distribution data.
 7. A method as definedin claim 6, wherein the second signature associated with thedistribution data is collected at a reference station not present in apanelist household.
 8. A method as defined in claim 1, furthercomprising identifying the second live media event.
 9. A method asdefined in claim 8, wherein the list identifies the live media events byname.
 10. A method as defined in claim 8, wherein identifying the secondlive media event comprises comparing a code present in the distributiondata to a first library mapping codes to at least one of a station name,a distributor name, a content owner or a program name.
 11. An apparatus,comprising: a comparator to compare distribution data to feed data, thedistribution data being representative of media information received ata detection site and the feed data being representative of respectivelive media events being provided to a distributor via correspondingsource feeds; and a list generator to generate a list identifying anon-the-fly switch occurring on a single channel between a first one ofthe live media events and a second one of the live media events at thedistributor level, the first live media event being a live media eventscheduled to be transmitted by the distributor at a first time, the listgenerator to generate the list by logging that the second live mediaevent is actually transmitted by the distributor at the first time. 12.An apparatus as defined in claim 11, wherein the comparator comprises asignature comparator to compare the distribution data to a signaturestored in a signature library, the signature being representative of oneof the live media events.
 13. An apparatus as defined in claim 11,wherein the distribution data and the feed data comprise signaturesgenerated using at least one of audio signaturing or video signaturing.14. An apparatus as defined in claim 11, wherein the comparatorcomprises a code comparator to identify at least one of a station name,a distributor name, a content owner or a program name.
 15. An apparatusas defined in claim 14, further comprising a code library to map aplurality of codes to respective ones of the station name, thedistributor name, the content owner or the program name.
 16. Anapparatus as defined in claim 11, wherein the list generator is togenerate the list without requesting additional information from asource of the distribution data.
 17. An apparatus as defined in claim11, wherein the comparator is to identify a match between thedistribution data and the feed data when at least a threshold number ofdistribution signatures collected from a media monitoring sitecorrespond to feed signatures collected from an on-site locationassociated with one of the live media events.
 18. An apparatus asdefined in claim 11, wherein the distributor is associated with a firstgeographical region different than a second geographical regionassociated with a second distributor that receives the source feeds. 19.A tangible computer readable medium comprising computer readableinstructions which, when executed, cause a processor to at least: accessfeed data representative of a plurality of source feeds being providedto a distributor, the source feeds being associated with respective livemedia events; access distribution data, the distribution data beingrepresentative of a plurality of distributions of the source feeds bythe distributor; and compare the distribution data to the feed data togenerate a list identifying an on-the-fly switch occurring on a singlechannel between a first one of the live media events and a second one ofthe live media events caused by the distributor, the first live mediaevent being a live media event scheduled to be transmitted by thedistributor at a first time, and generating the list comprises loggingthat the second live media event is actually transmitted by thedistributor at the first time.
 20. A tangible computer readable mediumas defined in claim 19, wherein the list identifies the live mediaevents by name.
 21. A tangible computer readable as defined in claim 19,further comprising identifying the live media events by comparing a codepresent in the distribution data to a first library mapping codes to atleast one of a station name, a distributor name, a content owner or aprogram name.
 22. A tangible computer readable as defined in claim 21,wherein identifying the live media events comprises comparing asignature associated with the distribution data to a second librarymapping signatures to at least one of a station name, a distributorname, a content owner or a program name.
 23. A tangible computerreadable medium as defined in claim 21, wherein the code present in thedistribution data is collected at a reference station not present in apanelist household.
 24. A tangible computer readable medium as definedin claim 19, further comprising identifying the live media events bycomparing a signature associated with the distribution data to a secondlibrary mapping signatures to at least one of a station name, adistributor name, a content owner or a program name.
 25. A tangiblecomputer readable medium as defined in claim 24, wherein the signatureassociated with the distribution data is collected at a referencestation not present in a panelist household.
 26. A tangible computerreadable medium as defined in claim 22, wherein the signature associatedwith the distribution data is collected at a reference station notpresent in a panelist household.
 27. A tangible computer readable mediumas defined in claim 19, wherein the plurality of distributions compriseat least one of an over-the-air television broadcast, cable delivery,satellite transmission, or Internet delivery.
 28. A method as defined inclaim 1, wherein at least a portion of the feed data and at least aportion of the distribution data are representative of a distribution ofthe first live media event, and collecting the portion of the feed dataand collecting the portion of the distribution data occur during thefirst live media event.
 29. A method as defined in claim 1, whereingenerating the list comprises determining which of the plurality ofsource feeds is being distributed by the distributor at a first time bycomparing the distribution data to the feed data.
 30. A method asdefined in claim 1, wherein the list identifies a first time at whichthe distributor starts distributing a first source feed associated withthe first one of the live media events and a second time at which thedistributor ends distribution of the first source feed or changes fromdistributing the first source feed to distributing a second source feedassociated with the second one of the live media events.